Salicylate activates AMPK and synergizes with metformin to reduce the survival of prostate and lung cancer cells ex vivo through inhibition of de novo lipogenesis.

Aspirin, the pro-drug of salicylate, is associated with reduced incidence of death from cancers of the colon, lung and prostate and is commonly prescribed in combination with metformin in individuals with type 2 diabetes. Salicylate activates the AMP-activated protein kinase (AMPK) by binding at the A-769662 drug binding site on the AMPK ß1-subunit, a mechanism that is distinct from metformin which disrupts the adenylate charge of the cell. A hallmark of many cancers is high rates of fatty acid synthesis and AMPK inhibits this pathway through phosphorylation of acetyl-CoA carboxylase (ACC). It is currently unknown whether targeting the AMPK-ACC-lipogenic pathway using salicylate and/or metformin may be effective for inhibiting cancer cell survival. Salicylate suppresses clonogenic survival of prostate and lung cancer cells at therapeutic concentrations achievable following the ingestion of aspirin (<1.0 mM); effects not observed in prostate (PNT1A) and lung (MRC-5) epithelial cell lines. Salicylate concentrations of 1 mM increased the phosphorylation of ACC and suppressed de novo lipogenesis and these effects were enhanced with the addition of clinical concentrations of metformin (100 µM) and eliminated in mouse embryonic fibroblasts (MEFs) deficient in AMPK ß1. Supplementation of media with fatty acids and/or cholesterol reverses the suppressive effects of salicylate and metformin on cell survival indicating the inhibition of de novo lipogenesis is probably important. Pre-clinical studies evaluating the use of salicylate based drugs alone and in combination with metformin to inhibit de novo lipogenesis and the survival of prostate and lung cancers are warranted.

Current applications of algorithmic artificial intelligence in interventional radiology: A review of the literature.

Artificial intelligence is a rapidly evolving area of technology whose integration into healthcare delivery infrastructure is predicted to have profound implications for medicine delivery in the 21st century. Artificial intelligence as it relates to healthcare is a term used to cover a wide scope of computer-based algorithms whose application varies from patient selection to enhancements in imaging and postoperative prognostication. This article reviews the literature to contextualise how AI is currently being implemented in interventional radiology. This review considers the literature from a preoperative, intraoperative and postoperative perspective.

Prevalence of internal carotid artery stenosis in ambulatory patients presenting for non-emergent percutaneous coronary angiogram in a single Australian centre.

INTRODUCTION: Association between coronary artery disease (CAD) and internal carotid artery stenosis (ICAS) could prove clinically relevant. However, evidence defining this association is currently inconclusive. Our study investigates the prevalence of ICAS in non-emergent, ambulatory patients presenting for PCA with suspected CAD in an Australian context. METHODS: Between February 2019 and June 2019, 121 consecutive participants were verbally consented and enrolled in our study. The data were analysed retrospectively. PCA and CUS were performed within 24 h of each other. Multinomial logistic regression assessed independent predictors for ICAS, with statistical significance set at P value < 0.05. Linear regression analysis correlated CAD and ICAS severity, with significance of a P-value < 0.05. Analysis was conducted using IBM SPSS 26 software (Chicago, Illinois). RESULTS: The final study included 121 patients (age 73 ± 9 years, 76.9% male). ICAS on CUS was present in 55.4% of participants for PCA. CAD was an independent risk factor for ICAS on multinomial logistic regression odds ratio 3.87 (P = 0.023). CAD severity (multi vessel disease) showed significant correlation with ICAS r = 0.22 (P = 0.014) using linear regression analysis. CONCLUSION: CAD is an independent predictor of ICAS, and severity of ICAS is correlated with CAD disease. However, most participants had only minor ICAS (16-49% diameter stenosis). Our findings are consistent with internationally published studies, suggesting their data are generalisable to the Australian population. Larger studies are needed to address the applicability of CUS screening in patients with advanced CAD.